We develop a methodology to predict box office
performance of a movie at the point of green-lighting, when only its script and
estimated production budget are available. We extract three levels of textual
features (genre and content, semantics, and bag-of-words) from scripts using
screenwriting domain knowledge, human input, and natural language processing
techniques. These textual variables define a distance metric across scripts,
which is then used as an input for a kernel-based approach to assess box office
performance. We show that our proposed methodology predicts box office revenues
more accurately (29% lower MSE) compared to benchmark methods.
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